4.5 Article

New processing methodology to incorporate marine halocarbons and dimethyl sulfide (DMS) emissions from the CAMS-GLOB-OCE dataset in air quality modeling studies

期刊

AIR QUALITY ATMOSPHERE AND HEALTH
卷 16, 期 4, 页码 681-689

出版社

SPRINGER
DOI: 10.1007/s11869-022-01301-0

关键词

Marine emissions; SMOKE; NetCDF Command Operator; CAMS-GLOB-OCE; CMAQ

向作者/读者索取更多资源

This study describes a method to adapt the CAMS-GLOB-OCE dataset for use in the preprocessor software SMOKE. The method involves updating file attributes and bilinear interpolation of compound emission fields. Testing the method with halocarbon and DMS emissions fields around Antarctica showed its potential for including marine emissions in air quality studies.
Oceans are the largest source of biogenic emissions to the atmosphere, including aerosol precursors like marine halocarbons and dimethyl sulfide (DMS). During the last decade, the CAMS-GLOB-OCE dataset has developed an analysis of daily emissions of tribromomethane (CHBr3), dibromomethane (CH2Br2), iodomethane (CH3I), and DMS, due to its increasingly recognized role on tropospheric chemistry and climate dynamics. The potential impacts of these compounds on air quality modeling remain, however, largely unexplored. The lack of a reliable and easy methodology to incorporate these marine emissions into air quality models is probably one of the reasons behind this knowledge gap. Therefore, this study describes a methodology to adapt the CAMS-GLOB-OCE dataset to be used as an input of the preprocessor software Sparse Matrix Operator Kernel Emissions (SMOKE). The method involves nine steps to update file attribute properties and to bilinearly interpolate compound emission fields. The procedure was tested using halocarbon and DMS emissions fields available within the CAMS-GLOB-OCE database for the Southern Ocean around Antarctica. We expect that this methodology will allow more studies to include the marine emissions of halocarbons and DMS in air quality studies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据